State of Low-Code and AI in 2025

Low-code platforms and AI are transforming software development and business operations in 2025. Here's what you need to know:

• Faster Development: AI-driven low-code tools reduce development time from weeks to hours. By 2026, 80% of software vendors are expected to embed generative AI.

• Improved Productivity: Enterprises are automating tasks, enhancing customer experiences, and cutting costs across industries like healthcare, finance, and retail.

• AI Features: Intelligent code generation, automated testing, and advanced data modeling are now standard, improving efficiency and application quality.

• Industry Impact: From reducing healthcare documentation time by 41% to cutting financial services cycle times by 20%, AI low-code tools are delivering measurable results.

• Challenges: Data security, training, and performance optimization remain critical barriers, but solutions like DevSecOps and unified platforms are helping organizations adapt.

Quick Stats:

• Generative AI could add $4.4 trillion annually to the global economy.

• 93% of executives plan to increase investments in AI-powered low-code solutions.

• AI is projected to generate 20% of all software code globally by the end of 2025.

Low-code and AI are reshaping industries, accelerating innovation, and driving economic growth. Businesses must focus on data integration, security, and training to fully leverage these tools.

The Future of Low-Code /No-Code Platforms with AI ...

Progress in Low-Code and AI Integration

AI has become a game-changer for low-code platforms, reshaping how software is developed and accelerating digital transformation efforts. Let’s dive into the advancements and the growing role of AI in this space.

Major Changes 2020-2025

Between 2020 and 2025, low-code platforms have undergone a major shift, making software development faster and more efficient. This evolution has reduced development times while increasing the functionality of applications.

Take PostNL, the largest parcel delivery service in the Netherlands, as an example. Handling 1.5 million delivery requests daily, they’ve integrated advanced Machine Learning Kit features to achieve sub-millisecond response times. Importantly, they’ve maintained strict data privacy by avoiding third-party services.

Here’s a quick look at how low-code capabilities have evolved during this period:

How AI Improves Low-Code Development

AI has introduced a suite of tools that make low-code development faster, smarter, and more efficient. These advancements not only simplify complex tasks but also improve the overall quality of applications.

Some key enhancements include:

• Intelligent Code Generation: AI now interprets developer intent, creating complex code snippets or even entire application components automatically.

• Automated Testing and Validation: AI systems monitor application quality in real time, flagging issues and ensuring best practices are followed before deployment.

• Advanced Data Processing: Integrated machine learning models enable platforms to analyze data and make decisions in real time.

Gartner estimates that by 2026, over 80% of independent software vendors will embed generative AI into their platforms. This rapid adoption highlights how AI is reshaping low-code development, allowing developers to focus on innovation while routine tasks and complex coding are handled by AI.

AI Features in Low-Code Platforms

AI is reshaping low-code platforms by boosting development speed and improving the quality of applications. Here's a look at the AI-driven features redefining the low-code space in 2025.

AI Code Generation

AI-driven code generation is now a central feature in low-code development. By the end of 2025, AI is expected to produce 20% of all software code globally, revolutionizing how applications are built.

"Code generation will continue to improve, impacting all tiers of modern applications. Expect to be able to build a full-stack containerized app from a single prompt." - Jason Bloomberg, President, Intellyx

Key advancements include:

• Anticipating and solving coding issues before they arise

• Producing clear, human-readable code that follows established standards

• Conducting real-time checks for security vulnerabilities and data privacy compliance

These improvements are also enhancing the way data models are created and managed.

Data Modeling with AI

AI is transforming data modeling by automating complex tasks and simplifying the structuring of data, making it easier to analyze and manage.

"Long-term, the world of coding will evolve from writing code to colloquially explaining a business problem to effectively an AI agent so that they can statically generate the right coding solution." - Paul Davis, Field CISO, JFrog

This evolution in data modeling works hand-in-hand with AI-powered testing tools, which are streamlining the development process even further.

AI Testing Tools

AI testing tools are slashing development timelines and making testing more efficient. For example, GE Healthcare cut its testing time from 40 hours to just 4 hours by using Functionize, saving 90% in labor.

"Working with Functionize has really transformed our QA practice at Kognitiv. We went from cringing when UI or code changes were done because of cumbersome and time-consuming processes to welcoming them due to the flexibility and mature self-healing logic that is built into the platform." - Duncan Anderson, VP of Quality Assurance

Similarly, GA Technologies reduced test authoring time by 95% with Autify, showcasing the potential of AI-driven testing to optimize workflows.

Industry Uses of AI Low-Code Platforms

These examples highlight how AI-driven low-code platforms are transforming various industries in 2025.

Healthcare Applications

Healthcare providers are using AI low-code tools to save time and improve patient care. For instance, AtlantiCare implemented Oracle's Clinical AI Agent, cutting documentation time by 41%. This freed up an extra 66 minutes per day for medical staff to focus on patients.

"I look forward to the day I no longer have to sit in front of a computer to treat my patient. Instead, I'm able to just have a regular conversation with my patient."
– Beth Kushner, CMIO at St. Joseph's Regional Medical Center

Cleveland Clinic is leveraging Microsoft's AI agents to streamline appointment scheduling and address health inquiries automatically. This is especially impactful, as healthcare professionals spend around 35% of their time on administrative tasks.

Financial Services Solutions

In the financial sector, AI low-code platforms are improving both efficiency and customer experiences. KeyBank, for example, reduced overall cycle times by 20%, built five applications in just six months, and automated 3,600 tasks.

"It's probably been 2+ years since we said, 'I can't do that.' With OutSystems, we can challenge ourselves to figure out a path to yes."
– Dominic Cugini, Chief Transformation Officer, KeyBank

Retail Implementation

AI low-code platforms are also reshaping retail by tackling inventory challenges and boosting customer satisfaction. Research highlights the impact:

These improvements are critical, as inventory mismanagement costs national retailers $1.75 trillion annually. AI tools enable real-time tracking, better demand forecasting, automated restocking, and just-in-time inventory management.

Implementation Challenges and Solutions

As low-code platforms and AI become more integrated, organizations are encountering several hurdles. Gartner estimates that by 2025, over 70% of new applications will be created using low-code or no-code development approaches.

Data Security Requirements

Security is a growing concern as AI-driven low-code platforms handle sensitive data. The challenge lies in managing the increased threat landscape introduced by a diverse range of users.

"Without carefully considering how AI tools store and protect proprietary corporate, customer, and partner data, organizations may be vulnerable to security risks, fines, customer attrition, and reputational damage. This is especially important for organizations in highly regulated environments, such as the public sector, financial services, or healthcare, that must adhere to strict external regulatory and compliance obligations." - David DeSanto, Chief Product Officer, GitLab

To address these threats, organizations can implement the following measures:

These steps form a foundation for tackling additional challenges related to people and system performance.

Talent and Training Needs

The combination of AI and low-code platforms introduces unique workforce development challenges. Programs like MindStudio Academy offer practical training paired with expert mentorship to address these gaps.

Organizations should:

• Create structured training programs that include hands-on projects and mentorship to prepare developers for leadership roles.

• Adopt DevSecOps practices to integrate security into every stage of development.

Performance Optimization

"When integrating AI into software development, security and data privacy emerge as critical challenges. AI systems often require access to large datasets, which can include sensitive and personal information. Ensuring the privacy and security of data is essential to prevent unauthorized access and breaches." - Ed Frederici, CTO, Appfire

To optimize performance, organizations can:

• Adjust AI models to suit specific use cases.

• Use compression techniques for deployed models to improve efficiency.

• Leverage cloud-based platforms for scalable GPU resources.

• Set up continuous monitoring systems to track performance metrics.

Additionally, context-aware filtering and rigorous training can help prevent intellectual property risks.

"It is also vital to train all users and developers interacting with Generative AI tools, making them aware of IP protection risks and policies." - Ed Charbonneau, Developer Advocate, Principal, Progress

Low-Code and AI After 2025

New AI Integration Methods

AI-driven low-code platforms are reshaping how applications are developed. Building on recent advancements, these platforms now allow AI to generate entire applications, changing the landscape of software development.

Developers will increasingly rely on multi-modal AI interactions, using text, images, and voice for more efficient workflows. Text commands, powered by natural language processing, will simplify code generation. Visual inputs will enhance design processes, while voice commands will improve accessibility and make development more streamlined.

"I believe AI will redefine how businesses engage with customers in 2025, moving beyond traditional interfaces to conversational, prompt-driven experiences. Instead of interacting with windows, buttons, or forms, we believe customers will increasingly engage with businesses through natural language prompts across text, audio, and video." - Tiago Azevedo, Chief Information Officer

These new methods of AI integration are setting the groundwork for rapid industry expansion.

Industry Growth Forecast

AI integration into low-code platforms is fueling economic growth. According to McKinsey, the economic impact of Generative AI (GenAI) is expected to be significant, with 93% of executives planning to boost their investments in AI-powered solutions.

Key growth trends include:

"Companies implementing GenAI will continue to face data integration, quality, and privacy challenges - and will be the biggest hurdles in driving GenAI scalability and success in 2025. Understanding data structure and integrating data will be key as organizations look to expand adoption and unlock the full value of AI." – Luis Blando, Chief Product & Technology Officer

The future of low-code platforms will also prioritize compliance and security. JJ McGuigan, Product Manager for Developer Tools and App Builder at Infragistics, explains:

"Low code platforms not only accelerate app development but also ensure that applications are built in alignment with legal and regulatory requirements. By integrating industry best practices into the development process, low code solutions will streamline compliance, enabling faster delivery of secure, compliant apps without sacrificing quality or oversight."

This shift is especially impactful for highly regulated industries, where built-in compliance features simplify development while maintaining strict security standards. The merging of no-code, low-code, and traditional coding capabilities is paving the way for a unified development ecosystem powered by AI that bridges business needs with technical requirements.

Conclusion: Key Steps for Success

AI low-code platforms are changing the way software is developed. To make the most of these tools, businesses need to focus on consolidating data, securing their systems, and developing skilled teams. These steps unlock the potential of GenAI to boost innovation and productivity.

"As we look ahead to software development in 2025, it's clear to us that AI and low-code will play a central role, providing organizations a foundation for agility, scalability, and infinite innovation possibilities." - Rodrigo Coutinho, Co-Founder and AI Product Manager

For successful adoption, organizations should focus on these key areas:

These focus points address ongoing challenges, especially around integration. Luis Blando, Chief Product & Technology Officer, highlights the importance of managing data effectively:

"To address the challenge of data silos and fully take advantage of GenAI's powerful capabilities, I believe IT leaders and their teams need to prioritize data consolidation and integration, which will require unified platforms. Safeguarding data privacy and mitigating bias will also be essential during this process to maintain trust and compliance."

Organizations should also prepare for emerging trends like multi-modal AI, which combines text, video, and images into development workflows. With 93% of executives planning to boost investments in AI-powered solutions, staying competitive means focusing on both technical skills and team expertise.

Looking ahead, advanced AI integration techniques will play a bigger role. Tools like RAG pipelines, vector databases, and autonomous AI agents are set to become critical for successful low-code implementations.

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